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library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
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library(readxl)library(tibble)# library(dplyr)# library(knitr)library(networkD3)library(tictoc)library(patchwork)# devtools::install_github("mattflor/chorddiag")library(chorddiag)dir.create(params$gdm_path, showWarnings =FALSE)fns <-sapply(1:6,function(i) {gsub(pattern ="X",replacement = i,x =file.path("input", "key_papers", params$key_paper) ) })############# Update `cX_key-paper.csv` from `TCA_Ch3_DOIs_Dataset_Theories_3_May_2024.xlsx`## This needs to be run manuallyif (FALSE) { all <- readxl::read_excel(file.path("input", "key_papers", "TCA_Ch3_DOIs_Dataset_Theories_3_May_2024.xlsx")) cluster <-names(all)[11:16]sapply(1:6,function(i) { all |> dplyr::filter(.data[[cluster[[i]]]] ==1) |> dplyr::select("DOI", "Authors", "Year", "Title") |> dplyr::filter(!is.na(DOI)) |> dplyr::mutate(DOI =gsub(pattern ="https://doi.org/",replacement ="",x = DOI ) ) |>write.csv(file = fns[i],row.names =FALSE ) } ) |>invisible()rm(all, cluster)}#############cluster <-lapply( fns,function(fn) {list(fn = fn,dois =read.csv(fn)$DOI ) })names(cluster) <-paste0("cluster_", 1:6)
Verification of key papers
The key papers should describe the cluster, exactly one cluster, and not multiple clusters. In other words, a key paper should only be in one cluster. If a key-papers is in multiple clusters, this would lead to an overlap wof the snowball corpus which is not caused by the cluster itself, but by the key paper which is in multiple clusters.
The papers are the following (the table will be empty if there are none):
For the second snowball search (S2) we use the corpus identified by S1 as the key-papers.
This needs to be enabled (eval = false needs to be changed to eval = true in the following code block) as the resulting data file is to large for github and running of the snowball search takes several hours.
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
MCO, MDV
and will therefore not be plotted!
Warning: Some values were not matched unambiguously: XK
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
MCO, NA
and will therefore not be plotted!
Warning: Some values were not matched unambiguously: XK
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
NA
and will therefore not be plotted!
Warning: Some values were not matched unambiguously: XK
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
MCO, NA
and will therefore not be plotted!
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
MCO, MDV
and will therefore not be plotted!
Warning: Some values were not matched unambiguously: XK
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
BMU, GIB, MCO, MDV, SMR, TUV, NA
and will therefore not be plotted!
Warning: Some values were not matched unambiguously: XK
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
NA
and will therefore not be plotted!
Warning: Some values were not matched unambiguously: XK
Warning in IPBES.R::map_country_codes(dplyr::mutate(dplyr::rename(as.data.frame(table(unlist(sapply(cluster[[name]]$snowball_1$nodes$author, : The following countries are not in the world dataset:
MCO, NA
and will therefore not be plotted!
This section looks at common works between the keypapers of each cluster as well as the whole corpus as obtained through a first generation snowball search using the provided keyworks.
Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html